Method and device for using gnss satellite trajectory extension data in mobile apparatus

ABSTRACT

A method and device for using satellite trajectory extension data in a mobile apparatus. The device in accordance with the present invention comprises an I/O interface and a microprocessor. The input/output (I/O) interface is used for obtaining at least one satellite navigation message for a satellite. The microprocessor is used for determining a propagating condition according to the satellite navigation message, estimating a plurality of parameters of a satellite trajectory prediction model according to the propagating condition to establish an estimated satellite trajectory predication model, propagating a set of satellite trajectory extension data by using the estimated satellite trajectory prediction model, computing acquisition assistance data according to the satellite trajectory extension data and acquiring signals of the satellite by using the acquisition assistance data.

CROSS REFERENCE TO RELATED APPLICATION

The present application is a continuation-in-part of U.S. patentapplication Ser. No. 12/103,387, entitled “Method and device forpredicting GNSS satellite trajectory extension data used in mobileapparatus”, filed on Apr. 15, 2008, which is entirely incorporated byreference herein.

TECHNICAL FIELD OF THE INVENTION

The present application relates to GNSS (Global Navigation SatelliteSystem) trajectory prediction.

BACKGROUND OF THE INVENTION

For a GNSS (e.g. GPS) receiver, sensitivity is a major performancecriterion. TTFF (Time to First Fix) is a representative standard forreceiver sensitivity. To speed up TTFF, a technique called AGPS(Assisted Global Positioning System) is developed to promote TTFFperformance. In an AGPS system, an assistant information is provided toa remote receiver so that the receiver can fix positions of satellitesin a shortened period of time. An important portion of the assistantinformation is satellite navigation message, such as ephemeris, orsatellite trajectory prediction data. The orbit determination technologyand satellite trajectory prediction can be implemented by using a plentyof ranging observations from reference ground network stations, whichcan be simply referred to as ground observations. Practically, theprocessing of ground observations is complicated, thus strongcalculation capability is required to execute the processing. As it isknown, there must be some prediction errors existing in satellitetrajectory extension, i.e. satellite trajectory predication, due toimperfect satellite trajectory prediction models. Therefore, predictionof the satellite trajectory cannot be extended unlimitedly. Currently,it is possible to predict the satellite trajectory for the up-coming 7to 14 days.

For the reason mentioned above, only a device with high calculationcapability such as a server is sufficient to support the satellitetrajectory prediction. The server calculates the predicted satellitetrajectory and passes the calculated satellite trajectory or equivalentdata set to an AGPS server. Then the AGPS server provides the satellitetrajectory prediction or equivalent data set to users throughconnection. According to the conventional schemes, it is difficult toexecute the satellite trajectory prediction on a mobile apparatus suchas a PDA (Personal Digital Assistant), a smart phone, a GPS apparatus orthe like. Therefore, it is an object of the present invention to providea method and a device for using GNSS satellite trajectory predictionthat can be implemented and resided on a host processor, which can beembedded in a mobile apparatus.

FIG. 10 is a flow chart showing a conventional process of obtaining andusing acquisition assistance (AA) data to acquire satellite signals. Asshown, estimated time (S1002), rough user position (S1004) of a remotereceiver and an estimated satellite position (S1006) are computed basedon satellite trajectory prediction such as long term orbit data at aserver or obtained from the broadcast ephemeris, which is valid in twohours in general. The remote receiver then computes the AA dataaccording to the position and time information in step S1010. Forexample, the remote receiver predicts possible Doppler shifts for thesatellite in view of the remote receiver. That is, the AA data at leastcomprises predicted Doppler shifts for the satellite in view of theremote receiver. In GPS, each satellite has its satellite signal leavethe satellite at a frequency of 1575.42 MHz. As known, the frequency ofthe satellite signal observed at the remote receiver will be shiftedabout ±4.5 KHz due to relative satellite motion. This is known as aDoppler shift. Then, a window of uncertainly range s provided for thesatellite based on the AA data in step S1020. This window defines acertain range of the Doppler shift and a range of code phase (i.e.certain code chips of the satellite signal) for the satellite. The sizeof the window is determined depending on the accuracy of the estimatedposition and time. In step S1030, the remote receiver acquires thesatellite signals using the AA data within the range of the window so asto avoid from searching the satellite in the widest range (i.e. all theDoppler shifts and all the code phases), which is referred to as “opensky searching”. In step S1040, a pseudorange, which is the time for thesatellite signal flying from the satellite to the remote receivermultiplied by the speed of light, in time domain and a Doppler shift infrequency domain are computed from the acquired satellite signal. Instep S1050, it is determined whether the number of the acquiredsatellites has been exceeded four. As well known in this field, to fix aposition, at least four satellites must be required. If so, then theuser position (i.e. the position of the remote receiver) can be fixed instep S1060. Otherwise, the remote receiver keeps searching (step S1070),and the process goes back to step S1020.

Under a circumstance where the valid ephemeris is not available, theremote receiver needs the assistance of the server in AGPS. The serverprovides the necessary data about the position and time to the receiverso that the receiver can compute the AA data so as to promote theperformance of TTFF. If the remote receiver fails to get in connectionwith the server in any manner, then the receiver cannot compute the AAdata and must execute the open sky searching (ex, searching all thesatellites in the open sky with all code phases and Doppler shifts.). Ascan be known, it takes a great period of time to do the open skysearching. This will adversely influence the TTFF.

SUMMARY OF THE INVENTION

In accordance with an aspect of the present invention, a method forusing GNSS satellite trajectory in a mobile apparatus comprises steps ofobtaining at least one satellite navigation message for a satellite;determining a propagating condition according to the obtained satellitenavigation message; estimating a plurality of parameters of a satellitetrajectory prediction model according to the propagating condition toestablish an estimated satellite trajectory predication model;propagating a set of satellite trajectory extension data by using theestimated satellite trajectory prediction model, computing acquisitionassistance data according to the satellite trajectory extension data;and acquiring signals of the satellite by using the acquisitionassistance data. The satellite navigation message can be previouslyobtained from satellite broadcasting or from an external source such asa server.

The method of the present invention further comprises determining if avalid ephemeris for the satellite is available. The acquisitionassistance data is computed according to the valid ephemeris if thevalid ephemeris is available. If the valid ephemeris is not available,the acquisition assistance data is computed according to the satellitetrajectory extension data as above.

In accordance with another aspect of the present invention, a device forusing GNSS satellite trajectory in a mobile apparatus comprises aninterface for obtaining at least one satellite navigation message for asatellite; and a microprocessor for determining a propagating conditionaccording to the satellite navigation message, estimating a plurality ofparameters of a satellite trajectory prediction model according to thepropagating condition to establish an estimated satellite trajectoryprediction model, propagating a set of satellite trajectory extensiondata by the estimated satellite trajectory prediction model, computingacquisition assistance data according to the satellite trajectoryextension data; and acquiring signals of the satellite by using theacquisition assistance data.

The microprocessor further determines if a valid ephemeris for thesatellite is available. The microprocessor computes the acquisitionassistance data according to the valid ephemeris if the valid ephemerisis available, while computes the acquisition assistance data accordingto the satellite trajectory extension data if the valid ephemeris is notavailable.

The satellite trajectory predication model used in the present inventioncan be any proper model such as a compact force model or an adjustedorbit model. The satellite navigation message comprises broadcastingephemeris and/or almanac as well as satellite position state vectorinformation and/or satellite velocity vector state information.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration showing a mobile apparatus having adevice for predicting and using trajectory extension data in accordancewith the present invention;

FIG. 2 is a flow chart showing a method for predicting trajectoryextension data in accordance with the present invention;

FIG. 3 is a schematic illustration showing various satellite navigationmessage sources for the mobile apparatus having the device forpredicting and using satellite trajectory extension data in accordancewith the present invention;

FIG. 4 schematically shows satellite navigation message segmentsobtained by the host device of FIG. 1;

FIG. 5 is a schematic diagram showing all satellite message segments ofTOE(2) to TOE(2 n) collected by the host device of FIG. 3;

FIG. 6 is a schematic diagram showing satellite arc information obtainedfrom satellite navigation messages such as the obtained broadcastingephemeris of satellite PRN01 in FIG. 4;

FIG. 7 is a flow chart showing a compact force model scheme forpropagating a set of trajectory extension data;

FIG. 8 is a flow chart showing an adjusted orbit model scheme forpropagating a trajectory;

FIG. 9 is a flow chart showing a method for using satellite trajectoryextension data to acquire satellite signals in accordance with thepresent invention; and

FIG. 10 is a flow chart showing a conventional process of obtaining andusing acquisition assistance (AA) data to acquire satellite signals.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1. is a schematic illustration showing a mobile apparatus 100 inaccordance with the present invention. The mobile apparatus 100 includesan antenna or antenna set 10 for receiving GNSS satellite signals and aGNSS receiver 20 as widely known in this field. The mobile apparatusfurther includes a host device 30, which can be a host part of a PDA, amobile phone, a portable multimedia player, a GPS apparatus or the like.The host device 30 has an I/O interface 32, an embedded microprocessor35, and a memory 37.

Also refer to FIG. 2, which is a flow chart showing a method forpredicting satellite trajectory extension data in accordance with thepresent invention. The GNSS receiver 20 collects broadcasting GNSSsatellite navigation messages such as broadcasting ephemeris and/oralmanac information as well as satellite position state vectors and/orsatellite velocity sate vectors via the antenna 10 in step S201. Thereceiver 20 transmits the satellite navigation messages to the hostdevice 30. The receiver 20 may just pass the satellite navigationmessages to the host device 30, and the microprocessor 35 embedded inthe host device 30 decodes the satellite navigation messages.Alternatively, the receiver 20 decodes the satellite navigation messagesand passes the decoded messages to the host device 30 directly. Then,the obtained and decoded satellite navigation messages are stored in thememory 37. Other satellite related information such as satellitegeometric information, earth orientation information, coordinatestransformation information, the earth potential model information (e.g.JGM3, EGM96 . . . etc.), the solar system ephemerides (e.g. JPL DE200 orDE405) and tidal models are all pre-stored in the memory 37 of the hostdevice 30.

FIG. 3 is a schematic illustration showing various satellite navigationmessage sources for the mobile apparatus 100. As shown, the mobileapparatus 100 also externally obtains satellite navigation messages. Inaddition to broadcast ephemeris and almanac received through the antenna10, the host device 30 obtains information for predicting satellitetrajectory extension data from an external source such as an orbitdatabase server 50 or another mobile apparatus 110 via network orwireless communication. It is noted that the host device 30 of themobile apparatus 100 can also predict satellite trajectory extensiondata based on history information saved therein. The microprocessor 35can also check consistency between the satellite navigation messagesfrom different sources (e.g. broadcasting ephemeris received via theantenna 10 and information from another mobile apparatus 110) beforeexecuting satellite trajectory extension data prediction.

FIG. 4 shows schematically satellite navigation message segmentsobtained by the host device 30 of FIG. 1. In this drawing, the shadowedchecks indicate that the satellite navigation message segments of thecorresponding time intervals are obtained. FIG. 4 shows the broadcastephemeris being received by the device 30. For example, as shown, for asatellite PRN01, the ephemeris segments of TOE(2) (i.e. ephemeris oftime t₂ to t₄) and TOE(2 k) (i.e. ephemeris of time t_(2k) to t_(2k+2))are obtained. That is, satellite messages of two discrete time intervalsare known. For another satellite PRN02, the ephemeris segments of TOE(2k) and TOE(2 n) are obtained. As generally known, orbit accuracy ofephemeris is better than an almanac. Accordingly, only the almanac inthe vicinity of time of almanac (TOA) can be used. The microprocessor 35then can use the known satellite navigation messages to predict theextended portion of satellite trajectory. Generally, multiple discreteephemeris segments provide more complete data set of satellitenavigation messages. However, even if only one ephemeris segment isobtained, it is also possible to predict the extended trajectory. Incontrast, if all ephemeris segments and/or almanac segments are known,satellite trajectory prediction can be done for a very long period withhigh accuracy. FIG. 5 is a diagram schematically showing that allsatellite message segments of TOE(2) to TOE(2 n) are collected by thehost device 30 of the FIG. 3. It is difficult to collect all satellitemessage segments of TOE(2) to TOE(2 n) by host device 30 shown inFIG. 1. However, through the help from the additional source such asanother mobile apparatus 110 or the orbit database server 50, the hostdevice 30 as shown in FIG. 3 can collect all satellite message segmentseasily.

Referring to FIG. 2, the microprocessor 35 computes the satellite arcsone by one according to the satellite navigation messages in step S203.A satellite arc means a segment of the satellite trajectory within atime interval corresponding to one segment of ephemeris or almanac. Asknown, ephemeris is updated every two hours. If the receiver 20 isturned on at any time between time of ephemeris t_(2k) to t_(2k+2), oncethe receiver 20 “hits” the satellite (i.e. the satellite is acquired),then the ephemeris during this period TOE(2 k) is obtained. That is, ifephemeris of time interval TOE(2 k) is received, then the satellite arcduring t_(2k) to t_(2k+2) is obtained.

FIG. 6 is a schematic diagram showing satellite arc information obtainedfrom satellite navigation messages such as obtained broadcastingephemeris of satellite PRN01 as shown in FIG. 4. Each of the solidcurves indicates that the ephemeris and/or almanac during thecorresponding intervals are obtained. This is called a satellite arc.The dashed curve indicates the approximated satellite trajectory. Asshown, satellite arc information during intervals TOE(2) (i.e. t₂ to t₄)and TOE(2 k) (i.e. t_(2k) to t_(2k+2)) are known. Then, many schemes canbe used to approach a set of trajectory extension data. For example, thesatellite arcs of TOE(2) and TOE(2 k) can be combined to simulate thecorresponding portion of the dashed curve. A satellite arc betweenTOE(2) and TOE(2 k) can be ignored here, or, if necessary, it can beextracted by an interpolation scheme. Then, by using the information,the extended satellite arc after TOE(2 k) can be predicted.

The satellite arc can be presented as PVT type (position, velocity, andtime) or Keplerian element type. In orbit determination, an exampletaking elliptic orbits, which may be characterized by orbital elementsincluding six Keplerian orbital elements, will be described as anexample. The six Keplerian orbital elements are semi-major axis element“a” defining the size of the orbit; eccentricity element “e” definingthe shape of the orbit; inclination element “i” defining the orientationof the orbit with respect to the earth equator; argument of perigeeelement “ω” defining where the low point, perigee, of the orbit is withrespect to earth surface; right ascension of ascending node element “Ω”defining the location of the ascending and descending orbit locationswith the earth's equatorial plane; and true/mean anomaly element “ν”defining where the satellite is with the orbit with respect to theperigee. Computing the Keplerian orbital elements from the position andvelocity vectors can be done because there is a one-to-onecorrespondence between the position/velocity vectors and the Keplerianorbital elements. The present invention can use the satellite positionand velocity at the epoch time or the six Keplerian orbital elements atthe same epoch time for propagating the satellite trajectory extensiondata.

Referring to FIG. 2 again, in step S203, the microprocessor 35 alsodetermines a propagating condition of a satellite trajectory predictionmodel. Two kinds of satellite trajectory prediction models will bedescribed as embodiments of the present invention. It is noted that theembodiments should not be taken as a limitation of the claim scope ofthe present invention. The first kind of satellite trajectory predictionmodel is called a compact force model, and the second kind is called anadjusted orbit model.

Equation of motion for describing a general orbit model of an artificialEarth satellite can be written as:

$\begin{matrix}{{\overset{¨}{\overset{\rightharpoonup}{r}} = {{{- {GM}}\frac{\overset{\rightharpoonup}{r}}{r^{3}}} + \overset{\rightharpoonup}{a}}},} & (1)\end{matrix}$

where the first term represents the central gravity term and {rightarrow over (a)} is the total perturbing acceleration. GM is the productof the constant of gravity and the mass of the Earth, {right arrow over(r)} is position vector of the geocentric radius of the satellite. Thissecond order differential equation system, in general, cannot be solvedanalytically, because the function of solution may be very complicated.Therefore, a compact perturbed force model or an adjusted orbit modelwith propagation algorithms have to be used. Equations for both thecompact force model and the adjusted orbit model are derived from theequation (1).

The compact force model or adjusted orbit model has to be estimated foreach subinterval arc and for each satellite individually. Thus, stepsS205 and S207 are executed by a sequential scheme. In step S205, themicroprocessor 35 uses the propagating condition and satellite relatedinformation to estimate a plurality of parameters of satellitetrajectory prediction model. In compact force model, the model can beparameterized by the components of perturbation forces. However, in theadjusted orbit model, orbital parameters stand for a series of unknownparameters which define the orbital equation of motion. Both situationswill be described later in detail. The satellite related informationsuch as satellite geometric information, coordinate transformationinformation, weight and volume of the satellite stored in a memory 37,can be also used to optimize the parameters.

In step S207, the microprocessor 35 uses a propagator (not shown), whichcan be implemented by a program built in the microprocessor 35, topropagate a set of satellite trajectory extension data by using thepropagating condition and the parameters of compact force model or theadjusted orbit model. In practice, the set of satellite trajectoryextension data include data of a plurality of time periods. Each timeperiod has a time length of at least one hour.

In step S209, the microprocessor 35 checks the validity of thepropagated satellite trajectory extension data. If the propagatedsatellite trajectory extension data are valid, the microprocessor 35sends the propagated satellite trajectory extension data to the receiver20 for acquiring or tracking the satellite next time in step S211. Thesatellite trajectory extension data may be converted to equivalent dataand stored first, and then the satellite trajectory extension data orthe equivalent data are sent to the receiver 20 at subsequent start-ups.Another advantage relates to the elimination of any error due to thetrajectory data being aged when relay to the satellite. Such error cancause the deviation and difference of the extension data and true orbit.This validity check avoids aging of trajectory extension data andlimiting the growth of any error.

FIG. 7 is a flow chart showing a compact force model scheme forpropagating a set of trajectory extension data. In an embodiment of thepresent invention, a closer look at the perturbation term {right arrowover (a)} in the equation (1) is made and the compact force model isused to summarize the various accelerations acting on the GNSSsatellite. The compact force model approximates the forces influencingtrajectory of a satellite so as to propagate the satellite trajectory.The forces includes earth gravity force component F_(Gravity), n-bodyforce component F_(N-body), solar radiation pressure component F_(Srad),earth radiation pressure component F_(Erad), ocean tide attractioncomponent F_(Ocen) _(—) _(tide), solid tide attraction componentF_(Sold) _(—) _(tide) and relative gravity component F_(relat). In thecompact force model, equation (1) is varied as:

$\begin{matrix}{\overset{¨}{\overset{\rightharpoonup}{r}} = {{{- {GM}}\frac{\overset{\rightharpoonup}{r}}{r^{3}}} + {\overset{\rightharpoonup}{a}}_{N - {bod}} + {\overset{\rightharpoonup}{a}}_{Srad} + {\overset{\rightharpoonup}{a}}_{Erad} + {\overset{\rightharpoonup}{a}}_{Ocean\_ tide} + {\overset{\rightharpoonup}{a}}_{Sold\_ tide} + {\overset{\rightharpoonup}{a}}_{relat} + \ldots}} & (2)\end{matrix}$

These force components are indicated by acceleration components inequation (2). For more precise numerical approximation, the entirepropagating interval [t₀, t_(f)] is divided into subintervals of auser-specified length. First in step S702, at least one satellite arc iscomputed from the obtained satellite navigation messages. Then, apropagating condition is determined according to the known satellite arcin step S705. That is, a satellite position is selected. In the firstsubinterval, the propagating condition is derived from one of thesatellite positions selected from the computed satellite arcs, which aredetermined in step S203 or S702. In any one of the subsequent intervals,the propagating condition is derived from the approximated solution ofthe previous subinterval. In another embodiment of the presentinvention, the microprocessor 35 can just only compute one satelliteposition from the collected satellite navigation messages as thepropagating condition, instead of computing the entire satellite arccorresponding to the satellite navigation messages.

The force components corresponding to the selected satellite positionare used to estimate a plurality of parameters of compact force modelF(X*, t_(i)) in step S710, where the X* can be substituted by the sixKeplerian elements, for example, at the reference epoch t_(k). Inaddition, the parameters of compact force model, such as forcecomponents, are estimated also with consideration of satellite relatedinformation including satellite geometric information and transformationinformation such as earth orientation factors and coordinates transformsbetween the celestial and terrestrial system. Then an equation of motion{dot over (X)}*=F(X*, t) is generated in step S730. The equation ofmotion is propagated by a propagator (step S740) to generate a segmentof satellite trajectory extension data X*(t_(i+1)) in step S750. Thesegment of satellite trajectory extension data X*(t_(i+1)) indicates thetrajectory from the time t_(i) to time t_(i+1). Then the microprocessor35 checks if the epoch t_(i+1) is the final epoch t_(f) of apredetermined period of time (step S760). If not, the microprocessor 35sets i=i+1 in step S765. That is, the microprocessor 35 is to computethe next segment of satellite trajectory extension data. After allsegments of satellite trajectory extension data during the predeterminedperiod of epoch to t₀ t_(f) are propagated, the entire extendedsatellite trajectory X*(t) is obtained (step S770). The displacementequation of motion X*(t) in S750 is an approximation of the satellitesorbit or trajectory around the earth. Thus, the X*(t) is actually areference trajectory. For a concern of fast computing, the referencetrajectory is utilized in short-term trajectory extension. Otherwise,the reference trajectory is utilized to predict a longer and moreaccurate extension by adjusting the model algorithm.

The microprocessor 35 can start to compute the extended trajectory oncethe required information is sufficient. For example, if the receiver 20receives the broadcast satellite navigation messages only for a while,the microprocessor 35 can compute the satellite trajectory extensiondata for an extended trajectory section even the receiver 20 losesconnection with satellite signals later. If additional satelliteinformation is input, the microprocessor 35 re-computes the extendedtrajectory. As mentioned, the microprocessor 35 may compute thesatellite trajectory extension data base on old data stored therein.

As mentioned, in addition to compact force model, the adjusted orbitmodel may also be used to predict the satellite trajectories. FIG. 8 isa flow chart showing an adjusted orbit model scheme for propagating atrajectory. An orbit in general sense is a unique (particular) solutionof orbital equation of motion. The adjusted orbit model uses a series ofunknown orbital parameters to define the satellite trajectory, whichdescribes the motion of the satellite. The orbital parameters aredefined as n parameters including the six Keplerian orbital elements ofthe orbits and additional dynamic parameters. In the adjusted orbitmodel, the additional dynamic parameters q_(m) describe the additionalperturbed accelerations acting on the GNSS satellite. For the adjustedorbit model, equation (1) is varied as:

$\begin{matrix}{\overset{¨}{\overset{\rightharpoonup}{r}} = {{{- {GM}}\frac{\overset{\rightharpoonup}{r}}{r^{3}}} + {\overset{\rightharpoonup}{a}\left( {a,e,i,\Omega,w,u,q_{1},q_{2},\ldots \mspace{14mu},q_{m}} \right)}}} & (3)\end{matrix}$

where the unknown parameters {p_(i)}={a, e, i, Ω, w, u, q₁, q₂, . . . ,q_(m)} may define the particular solution of orbit {right arrow over(r)}(t). The six Keplerian elements (or corresponding position andvelocity state vectors) at time t₀ are defined from the propagatingcondition.

As mentioned previously, the additional parameters q_(m) given inequation (3) are those dynamic parameters which have to be estimated foreach arc and each individual satellite in order to obtain a reliableorbital fit. For example, in GNSS orbit determination, the additionaldynamic parameters can be defined as the additional perturbedaccelerations and/or harmonic periodic accelerations (sine and cosineterms) acting on the GNSS satellite. In advanced orbit determination, auser may wish to parameterize the orbits additionally with otherstochastic parameters to improve the orbit quality.

As shown in FIG. 8, the satellite arc information is obtained fromsatellite navigation messages such as broadcasting ephemeris and/oralmanac (step S805). The microprocessor 35 then defines the propagatingcondition for the orbital parameters according to the satellite arcinformation (step S810). In step S820, the microprocessor 35 executesorbit determination based on the propagating condition and solves theorbital parameters, which are derived from equation (3). The parametersof the satellite trajectory prediction model are estimated also withconsideration of satellite related information including satellitegeometric information and transformation information such as earthorientation factors and coordinates transforms between the celestial andterrestrial system. In step S830, the microprocessor 35 checks if thesolved satellite arc fit the computed satellite arc from the obtainedsatellite navigation messages. If not, the procedure goes back to stepS810 to adjust the model, that is, to adjust the parameters. The aboveprocedure runs again. If it is determined as good fit, then thepropagation of the set of trajectory extension data is executed in stepS850 by the propagator (not shown). The propagation is implemented bynumerical integration, for example. Then, the entire set of trajectoryextension data X*(t₀: t_(f)) is obtained in step S860.

According to the present invention, the satellite trajectory predictioncan be done in the microprocessor 35, of which the calculationcapability is not so strong as a PC, in the host device 30 even when thereceiver 20 coupled to or incorporated with the host device 30 isdisconnected with the satellite signals.

FIG. 9 is a flow chart showing a method for using satellite trajectoryextension data to acquire satellites in accordance with an embodiment ofthe present invention. In this example, a mobile apparatus having thefunction of satellite predicting trajectory extension (e.g. the mobileapparatus 100 described above) may work in two modes. In a first mode, avalid broadcast ephemeris is received, and the ephemeris is used tocompute acquisition assistance (AA) data. In a second mode, the validephemeris or other equivalent information is not available. Under such acircumstance, the mobile apparatus 100 uses satellite trajectoryextension data created by the mobile apparatus 100 per se to compute theAA data. The details will be further described as follows. However, inanother embodiment, the mobile apparatus 100 can always use satellitetrajectory extension data created by the mobile apparatus 100 per se tocompute the AA data even if the valid ephemeris data is available.

Please refer to FIG. 9 and also FIG. 1. The mobile apparatus 100 shownin FIG. 1 is described herein as an example. The mobile apparatus 100attempts to fix the position thereof. If the mobile apparatus 100 isable to receive a broadcast ephemeris, which is usually valid within twohours, of a satellite, the received broadcast ephemeris is imported tothe microprocessor 35 of the host device 30 (step S910). In step S915,the received broadcast ephemeris is checked to see whether it is in thevalid period or not. In general, the valid period of a broadcastephemeris is two hours. If the ephemeris is valid, and then theephemeris is used to determine estimated time and position (andvelocity) of the satellite as well as a rough position of the mobileapparatus 100 in step S940. The data obtained in step S940 are used tocompute AA data in step S950. In step S970, the AA data is used toassist acquisition of satellite signals.

In step S915, if it is determined that the ephemeris is not valid (e.g.the broadcast ephemeris is expired) or even the ephemeris is notavailable, the satellite trajectory extension data, which is extended inthe mobile apparatus 100 per se as described above or equivalent dataare used. The process goes to step S930. The details will be furtherdescribed as follows.

In step S921, satellite navigation messages are obtained. Herein thesatellite navigation messages can be any kind of satellite navigationmessages, such as almanac, ephemeris (expired or not) or relevant datareceived from other external sources. In this case, those data arepreviously received and stored in the mobile apparatus 100. Asmentioned, history information obtained from old acquisitions, forexample, saved in the mobile apparatus 100 can also be used. In stepS923, a propagating condition is determined according to the obtainedsatellite navigation messages. In step S925, a satellite trajectoryprediction model is selected, and parameters of this model are estimatedaccording to the propagating condition so as to establish an estimatedsatellite trajectory prediction model based on the selected satellitetrajectory prediction model and the parameters. In step S927, themicroprocessor 35 propagates a set of satellite trajectory extensiondata by using the estimated satellite trajectory prediction model. Instep S930, the microprocessor 35 extracts the satellite trajectoryextension data or equivalent data derived from the satellite trajectoryextension data. In step S935, it is further checked whether those dataare available and valid. If not, a flag indicates that the satellitetrajectory extension data are not available is set in step S960.Otherwise, the satellite trajectory extension data propagated by themobile apparatus 100 per se are used to determine the estimated time andposition of the satellite as well as the user's rough position (i.e. therough position of the mobile apparatus 100) (S940). The data obtained instep S940 are used to compute AA data in step S950. In step S970, the AAdata is used to assist acquisition of satellite signals.

While the preferred embodiments of the present invention have beenillustrated and described in detail, various modifications andalterations can be made by persons skilled in this art. The embodimentof the present invention is therefore described in an illustrative butnot restrictive sense. It is intended that the present invention shouldnot be limited to the particular forms as illustrated, and that allmodifications and alterations which maintain the spirit and realm of thepresent invention are within the scope as defined in the appendedclaims.

1. A method for using GNSS satellite trajectory extension data in amobile apparatus, said method comprising steps of: obtaining at leastone satellite navigation message for a satellite; determining apropagating condition according to the obtained satellite navigationmessage; estimating a plurality of parameters of a satellite trajectoryprediction model according to the propagating condition to establish anestimated satellite trajectory prediction model; propagating a set ofsatellite trajectory extension data by using the estimated satellitetrajectory prediction model; computing acquisition assistance dataaccording to the satellite trajectory extension data; and acquiringsignals of the satellite by using the acquisition assistance data. 2.The method of claim 1, further comprising adjusting the propagatingcondition of the satellite trajectory prediction model to find anoptimized state of the satellite trajectory prediction model.
 3. Themethod of claim 2, wherein the parameters are adjusted withconsideration of at least one of either satellite geometric informationor coordinate transformation information.
 4. The method of claim 1,wherein the satellite trajectory prediction model is a compact forcemodel parameterized by perturbation force components, and the parametersrelate to the perturbation force components.
 5. The method of claim 4,wherein the satellite trajectory extension data set is divided into aplurality of trajectory segments and is propagated by integrating thetrajectory segments one by one.
 6. The method of claim 1, wherein thesatellite trajectory prediction model is an adjusted orbit modelparameterized by orbital parameters.
 7. The method of claim 6, whereinthe orbital parameters include Keplerian orbital elements.
 8. The methodof claim 7, wherein the orbital parameters further include a pluralityof dynamic parameters for describing addition perturbed accelerationsacting on the satellite.
 9. The method of claim 1, wherein the satellitenavigation message comprises information selected from a groupconsisting of broadcasting ephemeris, almanac, satellite position statevectors and satellite velocity state vectors.
 10. The method of claim 1,wherein the satellite navigation message is obtained from an externalsource other than the mobile apparatus.
 11. The method of claim 1,further comprising: determining if a valid ephemeris for the satelliteis available, wherein the acquisition assistance data is computedaccording to the valid ephemeris if the valid ephemeris is available,while is computed according to the satellite trajectory extension dataif the valid ephemeris is not available.
 12. A device for predictingGNSS satellite trajectory extension data used in a mobile apparatus,said device comprising: an interface for obtaining at least onesatellite navigation message for a satellite; and a microprocessor fordetermining a propagating condition according to the satellitenavigation message, estimating a plurality of parameters of a satellitetrajectory prediction model according to the propagating condition toestablish an estimated satellite trajectory predication model,propagating a set of satellite trajectory extension data by using theestimated satellite trajectory prediction model, computing acquisitionassistance data according to the satellite trajectory extension data,and acquiring signals of the satellite by using the acquisitionassistance data.
 13. The device of claim 12, wherein the microprocessorfurther adjusts the propagating condition of the satellite trajectoryprediction model to find an optimized state of the satellite trajectoryprediction model.
 14. The device of claim 13, wherein the parameters areadjusted with consideration of at least one of either satellitegeometric information or coordinate transformation information.
 15. Thedevice of claim 12, wherein the satellite trajectory prediction model isa compact force model parameterized by perturbation force components,and the parameters relate to the perturbation force components.
 16. Thedevice of claim 15, wherein the satellite trajectory extension data setis divided into a plurality of trajectory segments and is propagated byintegrating the trajectory segments one by one.
 17. The device of claim12, wherein the satellite trajectory prediction model is an adjustedorbit model parameterized by orbital parameters.
 18. The device of claim17, wherein the orbital parameters include Keplerian orbital elements.19. The device of claim 18, wherein the orbital parameters furtherinclude a plurality of dynamic parameters for describing additionalperturbed accelerations acting on the satellite.
 20. The device of claim12, wherein the satellite navigation message comprises informationselected from a group consisting of broadcasting ephemeris, almanac,satellite position state vectors and satellite velocity state vectors.21. The device of claim 12, wherein the satellite navigation message isobtained from an external source other than the mobile apparatus. 22.The device of claim 12, wherein the microprocessor further determines ifa valid ephemeris for the satellite is available, the microprocessorcomputes the acquisition assistance data according to the validephemeris if the valid ephemeris is available, while computes theacquisition assistance data according to the satellite trajectoryextension data if the valid ephemeris is not available.